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Link prediction based on generalized cluster information

Authors:

Jungeun Kim,

Minsoo Choy,

Daehoon Kim,

U KangAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 317 - 318

https://doi.org/10.1145/2567948.2578807

Published: 07 April 2014 Publication History

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Abstract

Understanding of which new interactions among data objects are likely to occur in the future is crucial for a deeper understanding of network dynamics and evolution. This question is largely unexplored except a local neighborhood perspective, partly owing to the difficulty in finding major factors which heavily affect the link prediction problem. In this paper, we propose LPCSP, a novel link prediction method which exploits the generalized cluster information containing cluster relations and cluster evolution information. Experiments show that our proposed LPCSP is accurate, scalable, and useful for link prediction on real world graphs.

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Cited By

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Potthast MGünther SBevendorff JBittner JBondarenko AFröbe MKahmann CNiekler AVölske MStein BHagen MDiaz FShah CSuel TCastells PJones RSakai T(2021)The Information Retrieval AnthologyProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462798(2550-2555)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462798
Han KYi MKim J(2019)Search Personalization in Folksonomy by Exploiting Multiple and Temporal Aspects of User ProfilesIEEE Access10.1109/ACCESS.2019.29270267(95610-95619)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2927026
Hou JPan HGuo TLee IKong XXia F(2019)Prediction methods and applications in the science of science: A surveyComputer Science Review10.1016/j.cosrev.2019.10019734(100197)Online publication date: Nov-2019
https://doi.org/10.1016/j.cosrev.2019.100197
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Index Terms

Link prediction based on generalized cluster information
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Author Tags

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Poster

Funding Sources

KAIST

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Potthast MGünther SBevendorff JBittner JBondarenko AFröbe MKahmann CNiekler AVölske MStein BHagen MDiaz FShah CSuel TCastells PJones RSakai T(2021)The Information Retrieval AnthologyProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462798(2550-2555)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462798
Han KYi MKim J(2019)Search Personalization in Folksonomy by Exploiting Multiple and Temporal Aspects of User ProfilesIEEE Access10.1109/ACCESS.2019.29270267(95610-95619)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2927026
Hou JPan HGuo TLee IKong XXia F(2019)Prediction methods and applications in the science of science: A surveyComputer Science Review10.1016/j.cosrev.2019.10019734(100197)Online publication date: Nov-2019
https://doi.org/10.1016/j.cosrev.2019.100197
Ma YCheng GLiu ZLiang X(2016)Link Prediction Based on Clustering Information in Scientific Coauthorship Networks2016 IEEE First International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2016.58(668-672)Online publication date: Jun-2016
https://doi.org/10.1109/DSC.2016.58
Ahmed CElKorany ABahgat R(2016)A supervised learning approach to link prediction in TwitterSocial Network Analysis and Mining10.1007/s13278-016-0333-16:1Online publication date: 2-May-2016
https://doi.org/10.1007/s13278-016-0333-1
Srinivas VMitra PSrinivas VMitra P(2016)IntroductionLink Prediction in Social Networks10.1007/978-3-319-28922-9_1(1-14)Online publication date: 23-Jan-2016
https://doi.org/10.1007/978-3-319-28922-9_1
Chawuthai RTakeda HHosoya T(2015)Link Prediction in Linked Data of Interspecies Interactions Using Hybrid Recommendation ApproachSemantic Technology10.1007/978-3-319-15615-6_9(113-128)Online publication date: 21-Feb-2015
https://doi.org/10.1007/978-3-319-15615-6_9

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Link prediction in complex networks based on cluster information

Discovering cluster evolution patterns with the Cluster Association-aware matrix factorization

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